Compressed Video Streams: Network Constrained Smoothing

Transcription

1 Compressed Video Streams: Network Constrained Smoothing Chris Bewick Rubem Pereira Madjid Merabti. June 1999 Distributed Multimedia Systems Group Liverpool John Moores University Byrom St, Liverpool, England. L3 3AF Abstract In this paper we introduce a new approach for dealing with variable bit rate (VBR) compressed video streams, such as the ones transmitted by Video-On- Demand servers. Bit rate bursts are exhibited along multiple time scales during transmission, and are not fully captured in the VBR flow specifications for current QoS networks such as the Tenet Real Time Protocol Suite [1] and ATM policing components [2]. These flow specifications are essential to simultaneously maximise network utilisation and minimise the probability of cell or packet loss that ends in QoS degradation. By balancing the early transmission of frames, via work-ahead smoothing, against the necessary buffer requirements and delay bounds, the optimal smoothing algorithm [3] stand as a theoretical technique for reducing bit rate variability. The scheme presented in this paper extends existing results in the area of smoothing and buffering techniques to incorporate network constraints. We consider how a network profile may be generated for a connection path, and how efficiently a smoothed video stream may be scheduled for reservation given this information. Our results will show how the network as a whole is able to support more connections at any one time, and strongly hint at the further reaching implications of the flow specification mapping techniques. 1. Introduction Distributed multimedia applications are going to play an important role in shaping the type of service provided by future networks. Commercial potential is enormous and already the race has begun, with cable and telecommunication companies in the lineup. One of the most challenging of these multimedia applications is VOD (Video-on- Demand). Customers are able to choose a video and receive it at the point of request with very delay. The client makes the request for a video to a video-server across an

2 intermediate distribution network. The video-server can then assess whether the system has sufficient resources to service the request for the duration of the connection. The assessment involves the video-server interacting with the distribution network for the determination of the best connection route from source to sink, as well as one that can be reliably reserved. During reservation, the key issue is the support for QoS by the underlying network sub-systems, allocating the least amount of resources as possible per connection without compromising connection QoS requirements. This is not a trivial problem: due to the high bandwidth associated with raw digitised videos, compressed techniques are used that introduce variability to the bit rate. The bandwidth is greatly reduced after compression, by a order of magnitude of 50 or more for sustainable high-quality PAL video. The bit rate variance (burstiness) is born out of the compression technique needed to produce a constant perceivable level of quality in the video. Here the compression algorithm produces variability on multiple time-scales, from the individual frame sizes varying both periodically and in response to image content, to a larger time-scale where scene changes take place. During a scene changes less compression is possible, since there is less correspondence between a frame and the ones before it. Scene changes may occur infrequently and then later in the video, a group clustered together, attributing to another layer in the multiple time-scale model. These are all important issues when considering how QoS is to be efficiently and effectively provided for VBR, and in particular the idea of multiple time-scale layering of multimedia streams. 2. QoS issues arising from VBR For provisions of QoS guarantees the networks needs to know the characteristics of the traffic for new connections. This information typically comes in a number of parameters, such as peak and average cell rate amongst others. Traffic shaping mechanisms such as the leaky bucket, which is used in ATM networks, endeavour to force network users to comply with the traffic parameters agreed upon during connection establishment. The sender is responsible for describing the characteristics accurately, and the network agrees to provide QoS as long as the traffic follows the specification. In cases where the live connection breaks the agreement, the leaky bucket mechanism discards cells on a per-stream basis, degrading the QoS until the stream becomes compliant again. This scheme is biased toward the network provider, in that it is the misbehaving connection that is penalised in order to prevent other

3 streams from losing their QoS guarantees as a result of cell loss or delay. The problem lies in the fact that, with compressed video, the network users are not fully aware of the best way to describe the traffic characteristics to the network. A result of this is that either the network is under-utilised, with eventual higher costs per connection, or packets are dropped, with degradation of QoS. We tackle this problem by developing a mapping scheme that allows new connections traffic characteristics to be specified on a per interval basis, such that in each interval the stream bit-rate is constant. 3. Related work VOD architecture can be divided into the following subsystems: The Video Server The Distribution Network The User Equipment The strict QoS and high throughput required by real time multimedia connections impact heavily on the design complexity of VOD systems. One relevant issue is the effect that the design of each subsystem has on others. One example is how the video server method of per-stream buffer usage influences the bit-rate at a given time, and so affects the optimal bandwidth reservation scheme for the network. This is intended to allow client and server to better cope with data transfer variability and to remove delay jitter [4]. The buffer is put to use by applying smoothing algorithms to the untouched bit-rate trace of a compressed video stream. The simplest smoothing technique is temporal buffering where the result is the peaks and troughs are flattened but a delay is introduced to the connection. The delay is because of the way that the buffer operates, always making sure that it is displaying video frames slightly behind when they are actually received. There are two more basic smoothing techniques, and another of them is smoothing by aggregation (or statistical multiplexing). This is achieved when a large number of independent streams share a given network bandwidth, and as the number of streams becomes large, the aggregate bandwidth requirement tends to a Normal distribution. This does not introduce any kind of delay, but alone can only provide statistical QoS guarantees to each connection. The number of simultaneous streams would need to be extremely large (i.e. tending to infinity) before the aggregate bandwidth becomes predictable, as it complies with the Normal Distribution. This paper deals with the final basic smoothing technique, smoothing by workahead. The method is the most active from the point of view of the processing

4 needed for calculation and the effect on traffic shape. It works by transmitting traffic peaks in advance of when the video information is due to be displayed, constrained by the size of the buffer and whether the data is available to be sent [5]. The networking benefits come from the bandwidth reserved may be relieved during certain periods of the connection duration and exploit the low load level of other periods [6]. An optimal smoothing algorithm, from the point of view of a single stream, has been reported in [3]. Smoothing of this type, without considering the loading on the distribution network is unlikely to optimise the network and, as a result, the whole system. Our method extends the optimal smoothing groundwork to take into account the network loading and consequently other streams multiplexed across the network. 4. Network Constrained Smoothing scheme In the scheme we present here, compressed video streams are smoothed in order to present per interval CBR characteristics. Without any workahead smoothing, CBR segments would mean that the perceivable end quality of the video would fluctuate with the complexity and movement of the video content [7]. In this case, workahead smoothing is applied to calculate CBR segments for transmission of VBR encoded video. The end result being a list of CBR segments, each with its own bandwidth requirement, and is called the transmission schedule. Although the variable nature of the stream has not been eradicated, the segmentation has accurately dissected the traffic shape into a set of parameters. The original unsmoothed trace is far too complex to be described by its shape, and typically is simply parameterised into overall average and peak bit-rates. The workahead smoothing generates a set of easily manageable measurement units about the video stream, and in particular they can be policed with ease under QoS such as ATM that provide support for leaky bucket constraints. The number of segments generated is minimised to akin the original VBR traffic to CBR as much as possible, reaping the inherent benefits of CBR from a QoS point of view. It is relatively simple to accurately and efficiently reserve network resources for CBR, an aspect so strong that there are even papers arguing the need for VBR and investigating video quality parameters for encoding. [8]. Here we consider how the segments are actually selected and under what criteria. The choice of CBR segment length and bit-rate are constrained by a number of factors: the content of the video stream, the available buffer space, the network

5 loading and consequently the network path. This paper focuses on using information about network loading to improve overall network efficiency, and points the way to incorporating routing algorithms. Routing would be performed by choosing the best route that can support the transmission schedule made up of the CBR intervals. Each router, therefore, will have a complete bit-rate reservation map of all the connections it is supporting [9]. The information is fed back to servers attempting new connections so that they can adapt to network loading by smoothing video streams in order to relieve the bit-rate in intervals of high network loading. In this paper, a two level smoothing is proposed: Firstly to find the best route capable of supporting an optimally smoothed traffic map. Secondly, the information about loading along the route is used to exploit less loaded intervals and reduce the loading at higher loaded times. The techniques used to determine the less loaded time intervals are modular and plug-in by nature to our overall scheme. The degree of freedom for changing the transmission schedule is determined by the size of the buffer and the time when video data is available for transmission (e.g. availability assumed to be immediate for pre-compressed video). Maximum cumulative data receivable without buffer overflow Cumulative data consumed by client time 5. Network resource allocation In this section we consider a suitable QoS model for making use of the video transmission schedules. The model is based on the Renegotiated CBR (RCBR) service [10], where the only performance gains are achieved strictly through statistical multiplexing. This is not the ideal situation for clients and VOD operators alike since the streams may often suffer frame loss and degraded QoS at periods of peak loading.

6 Since the smoothing of each stream under the scheme herein improves the balance of workload between network nodes, it follows that a higher level of multiplexing can be attained before any renegotiations are needed. This is by the fact that the overall network traffic is more evenly spread, the bursts of one stream more likely to coincide with the troughs of another stream. During periods when many connections are being reserved or have been reserved, the connection admission control permits a greater number of deterministic connections to be served simultaneously, and consequently an improved level of multiplexing. Network performance benefits are reaped from the fact that network bandwidth is intelligently allocated, and so the maximum possible number of connections across the network as a whole is increased. 6. Theoretical case study This section compares the optimal smoothing to a network constrained smoothing for a traffic level that is close to saturating a switch along the connection path. This case study shows a case where the new scheme has an immediate effect on the network performance. Video Server ATM switch Client Client The dotted line in the diagram represents a connection path that the video server is seeking to reserve. The switch in the centre is in a highly loaded state, and is supporting a number of connections. The following graph shows the aggregate of all the connections being supported at the switch, and a map of the new optimally

7 smoothed connection is overlaid on top. The straight dotted line represents the maximum throughput that the switch can cope with. Aggregate after new connection Aggregate before new connection New connection time Note that the y-axis represents the bit-rate and does not start from zero for the two aggregates, illustrating fluctuation of throughput at the switch. The new connection is drawn at the same unit scale, but starts from zero on the y-axis. In this first graph the new connection was smoothed using optimal smoothing, and it breaks the limit of the switch by its shape. The following graph shows how the network constrained smoothing of the new connection can better fit the aggregate traffic in such a way that it does not break the throughput limit. The total video data to be transmitted is exactly the same in both smoothed connections (i.e. the area under the graph), the only difference is the smoothing technique. Aggregate after new connection Aggregate before new connection New connection time

8 The constraint for how much we are allowed to re-smooth is the buffer size, and in this case it allows the new smoothing to avoid breaking the switch limit on throughput. Note that in this example, the switch that was under considereation had the highest loading for the entire connection duration compared to the other switches in the connection route. The method of measuring the network profile along the route is important to how the connection is re-smoothed, and smoothing for the highest loaded switch is likely not an optimal solution. 7. Evaluation The case study examines how network constrained smoothing can be employed to avoid congestion at the most highly loaded switch along a connection path. The optimal smoothing produces smoothed streams with minimised rate variability, but this still generates congestion when a number of streams are multiplexed through a network switch. A connection s QoS guarantees may be under threat when too many high rate segments from one stream to the next coincide. The case involves precisely this, and then goes on to show how by, taking advantage of buffer freedoms, the congestion may be avoided. Our step-by-step algorithm for numerically calculating the network constrained smoothing is currently in the stages of development, but the results back up the formulation. Indeed there is more than one way to perform the smoothing, balancing priorities such as minimising the number of CBR segments and incorporating network link speed limitations. Further experimentation is needed to explore the effects of network constrained smoothing on a larger scale. The results highlight how the scheme smoothes the video streams as well as the network profile. The effect is to smooth overall network loading as connections are added, each new stream smoothed accordingly. 8. Conclusion In this paper we have demonstrated a new scheme for transmitting pre-compressed VBR video streams under network constrained smoothing. The ideas embodied draw from QoS flow specification, smoothing and network profiling. Within a framework of support for VOD, the new smoothing technique enables the network operator to provide a bigger and more reliable service through increased multiplexing and better

9 distribution of network loading. The resource savings can be passed on the video server operator by way of cheaper charges and better service at peak times. The results presented in this paper show how, by taking advantage of the workahead smoothing and the buffering allowances, the shape of the traffic may be manipulated to better suit the expected network loading. This is important in terms of the forthcoming QoS networks that are able to cope with a more complex flow specification other than simply peak and average rates. The relatively poor performance of other optimal smoothing techniques show how the smoothing technique and network load profile can not be considered mutually exclusive. Our evaluation explains this, and goes further to suggest directions for further work investigating the relationship between the network profile and flow specification. We have shown that our scheme allows routers to support more connections than was possible with previous smoothing schemes. This leads to more efficient use of QoS networks, thus promoting the development of distributed multimedia services. References [1] Ferrari, D. (1998). The Tenet experience and the design of protocols for integrated-services internetworks. Multimedia systems 6(3): [2] Campbell, A., G. Coulson, et al. (1994 April). A Quality of Service Architecture. ACM SIGCOMM Computer Communication Review 24(2): [3] Salehi, J., Z.-L. Zhang, et al. (1998). Supporting Stored Video: Reducing Rate Variability and End-to-End Resource Requirements Through Optimal Smoothing. IEEE/ACM Trans. Networking 6(4). [4] Wrege, D. E., E. Knightly, et al. (1996). Deterministic Delay Bounds for VBR Video in Packet-Switching Networks: Fundamental Limits and Practical Tradeoffs. EEE/ACM Transactions on Networking 4(3): [5] Wu, T. and E. Knightly (1999). Buffering vs. Smoothing for End-to-End QoS: Fundamental Issues and Comparison. Proceedings of Performance '99, Istanbul, Turkey.

10 [6] Knightly, E. and P. Rossaro (1997). On the Effects of Smoothing for Deterministic QoS. Distributed Systems Engineering Journal: Special Issue on Quality of Service 4(1): [7] Krunz, M. and S. K. Tripathi (1997). Exploiting the Temporal Structure of MPEG Video for the Reduction of Bandwidth Requirements. IEEE INFOCOM'97, Japan. [8] Grossglauser, M., S. Keshav, et al. (1995). The Case Against VBR. Proc. 5th Intl. Workshop on Network and Operating System Support (NOSSDAV '95), Durham, New Hampshire. [9] Knightly, E. and J. Qiu (1998). Measurement-Based Admission Control with Aggregate Traffic Envelopes. IEEE ITWDC '98, Ischia, Italy. [10] Grossglauser, M., S. Keshav and D. N. C. Tse (1997). RCBR: A simple and efficient service for multiple time-scale traffic. IEEE/ACM Trans.Networkings, 5(6):